Open AccessBook
Genetic Programming: On the Programming of Computers by Means of Natural Selection
TLDR
This book discusses the evolution of architecture, primitive functions, terminals, sufficiency, and closure, and the role of representation and the lens effect in genetic programming.Abstract:
Background on genetic algorithms, LISP, and genetic programming hierarchical problem-solving introduction to automatically-defined functions - the two-boxes problem problems that straddle the breakeven point for computational effort Boolean parity functions determining the architecture of the program the lawnmower problem the bumblebee problem the increasing benefits of ADFs as problems are scaled up finding an impulse response function artificial ant on the San Mateo trail obstacle-avoiding robot the minesweeper problem automatic discovery of detectors for letter recognition flushes and four-of-a-kinds in a pinochle deck introduction to biochemistry and molecular biology prediction of transmembrane domains in proteins prediction of omega loops in proteins lookahead version of the transmembrane problem evolutionary selection of the architecture of the program evolution of primitives and sufficiency evolutionary selection of terminals evolution of closure simultaneous evolution of architecture, primitive functions, terminals, sufficiency, and closure the role of representation and the lens effect Appendices: list of special symbols list of special functions list of type fonts default parameters computer implementation annotated bibliography of genetic programming electronic mailing list and public repositoryread more
Citations
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Journal ArticleDOI
A new optimization method based on COOT bird natural life model
Iraj Naruei,Farshid Keynia +1 more
TL;DR: A new meta-heuristic method is proposed that inspires the behavior of the swarm of birds called Coot, and it is shown that this algorithm is capable to outperform most of the other optimization methods.
Journal ArticleDOI
Bird mating optimizer: An optimization algorithm inspired by bird mating strategies
TL;DR: A novel version of EAs, bird mating optimizer (BMO), is proposed for continuous optimization problems which is inspired by mating strategies of bird species during mating season and represents a competitive performance to other EAs.
Journal ArticleDOI
The Automatic Acquisition, Evolution and Reuse of Modules in Cartesian Genetic Programming
TL;DR: A generalization of the graph- based genetic programming technique known as Cartesian genetic programming (CGP) by utilizing automatic module acquisition, evolution, and reuse, which shows the new modular method evolves solutions quicker than the original nonmodular method, and the speedup is more pronounced on larger problems.
Journal ArticleDOI
Combination of Video Change Detection Algorithms by Genetic Programming
TL;DR: This paper investigates how state-of-the-art change detection algorithms can be combined and used to create a more robust algorithm leveraging their individual peculiarities and exploits genetic programming (GP) to automatically select the best algorithms, combine them in different ways, and perform the most suitable post-processing operations on the outputs of the algorithms.
Journal ArticleDOI
Maintainability defects detection and correction: a multi-objective approach
TL;DR: This paper proposes a two-step automated approach to detect and then to correct various types of maintainability defects in source code, using Genetic Programming to allow automatic generation of rules to detect defects, thus relieving the designer from a fastidious manual rule definition task.
References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Journal ArticleDOI
Optimization by Simulated Annealing
TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
Book ChapterDOI
Learning internal representations by error propagation
TL;DR: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion.
Book
Ecological Diversity and its Measurement
TL;DR: In this paper, the authors define definitions of diversity and apply them to the problem of measuring species diversity, choosing an index and interpreting diversity measures, and applying them to structural and structural diversity.
Book
The perception: a probabilistic model for information storage and organization in the brain
TL;DR: The second and third questions are still subject to a vast amount of speculation, and where the few relevant facts currently supplied by neurophysiology have not yet been integrated into an acceptable theory as mentioned in this paper.